Research Article
EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK
Abstract
Soil plays a vital role in the climate system.
This paper performs a hybrid methodology that consists of particle swarm optimization
(PSO) and artificial neural network (ANN) to estimate soil moisture (SM) by
considering different parameters that include air temperature, time, relative
humidity and soil temperature. Besides, this paper investigates the effects of
the parameters of PSO-ANN by utilizing from the response surface. PSO algorithm
is involved in the process of changing the weights of ANN. The coefficient of
determination and mean absolute error are chosen to measure the performance of
the performed hybrid PSO-ANN. The numerical results show that hybrid PSO-ANN is
applied to estimate SM successfully.
References
- [1] Shukla G., Garg R. D., Srivastava H. S., Garg P. K., “An effective implementation and assessment of a random forest classifier as a soil spatial predictive model”, International Journal of Remote Sensing, 39(8), 2637-2669, 2018. [2] Qu Y., Qian X., Song H., Xing Y., Li Z., Tan, J., “Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing”, Water, 10, 423, 2018. [3] Moosavi V., Talebi A., Mokhtari M. H., Hadian M. R., “Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches”, International journal of remote sensing, 37(23), 5605-5631, 2016. [4] Kundu D., Vervoort R. W., van Ogtrop F. F., “The value of remotely sensed surface soil moisture for model calibration using SWAT”, Hydrological Processes, 31(15), 2764-2780, 2017. [5] Yang Q., Zuo H., Li W., “Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region”, Plos One, 11(3), 2016. [6] Eberhart R. and Kennedy J., “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium Micro Machine and Human Science, Nagoya, Japan, 39-43, 1995. [7] https://www.utm.utoronto.ca/geography/resources/environmental-datasets, 04.02.2019
Year 2020,
Volume: 9 Issue: 1, 186 - 194, 30.01.2020
Abstract
References
- [1] Shukla G., Garg R. D., Srivastava H. S., Garg P. K., “An effective implementation and assessment of a random forest classifier as a soil spatial predictive model”, International Journal of Remote Sensing, 39(8), 2637-2669, 2018. [2] Qu Y., Qian X., Song H., Xing Y., Li Z., Tan, J., “Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing”, Water, 10, 423, 2018. [3] Moosavi V., Talebi A., Mokhtari M. H., Hadian M. R., “Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches”, International journal of remote sensing, 37(23), 5605-5631, 2016. [4] Kundu D., Vervoort R. W., van Ogtrop F. F., “The value of remotely sensed surface soil moisture for model calibration using SWAT”, Hydrological Processes, 31(15), 2764-2780, 2017. [5] Yang Q., Zuo H., Li W., “Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region”, Plos One, 11(3), 2016. [6] Eberhart R. and Kennedy J., “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium Micro Machine and Human Science, Nagoya, Japan, 39-43, 1995. [7] https://www.utm.utoronto.ca/geography/resources/environmental-datasets, 04.02.2019
There are 1 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Industrial Engineering |
| Journal Section | Industrial Engineering |
| Authors | |
| Publication Date | January 30, 2020 |
| Submission Date | February 19, 2019 |
| Acceptance Date | December 5, 2019 |
| Published in Issue | Year 2020 Volume: 9 Issue: 1 |
